MapReduce Task Scheduling – Simplified Diagram

Here’s a simplified explanation of MapReduce task scheduling architecture:

graph TD
    A["Client Job Submission"] --> B["JobTracker"]
    B --> C["Resource Manager"]
    C --> D1["TaskTracker 1"]
    C --> D2["TaskTracker 2"]
    C --> D3["TaskTracker n"]
    D1 --> E1["Map Tasks"]
    D1 --> F1["Reduce Tasks"]
    D2 --> E2["Map Task (1 slot)"]
    D2 --> F2["Reduce Tasks (2 slots)"]
    D3 --> E3["Map Tasks"]
    D3 --> F3["Reduce Tasks"]

    %% Add descriptions
    style B fill:#90EE90
    style C fill:#FFB6C1
    style D1 fill:#87CEEB
    style D2 fill:#87CEEB
    style D3 fill:#87CEEB

Key components:

JobTracker: Central component that manages job scheduling and monitors progress
Resource Manager: Handles resource allocation across the cluster
TaskTrackers: Run on worker nodes, execute map and reduce tasks, and report status to JobTracker.
Map Tasks: Process input data in parallel, creating key-value pairs
Reduce Tasks: Aggregate and process mapped data to produce final output

This architecture enables distributed processing of large datasets across multiple nodes in a cluster, providing fault tolerance and scalability.

MapReduce Task Scheduling – Simplified Diagram

Manan Younas